Fixed up some bugs in the code, improved a function that adds bias term, and improved the markdown documentation/explanations

Added new explanations for the regression model, and cleared some TODOs

Notebook animation of the K-means algorithm

improved documentation and explanations further

refactored some code, and tidied up descriptive explanations

WIP Exercise 7: Completed all K-means and a bit of PCA

Exercise 6 WIP: completed SVM exercises, left with spam classification

Added new reference on the topic of numerical optimization, and updates some work documentation in a markdown cell

WIP: Completed ex5, with largely correct results, but still some discrepancies to be fixed

Fixed a bug in computation of points for the visualization of regression, that previously resulted in a wrong plane being drawn

Eliminated bug in neural network forward pass function that resulted in use of a global variable rather than a local variable for computation

Completed programming exercise 4, but expect future improvements to the code

Removed a redundant np.sum() call within the logistic regression cost function

Updated readme

Updated exercise 3 with explanation for one-vs-all classifier discrepancy based on different optimization algos